#coding=utf-8

from numpy import *
import operator
from os import listdir

def createDataSet():
	group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
	labels = ['A', 'A', 'B', 'B']
	return group, labels

def classify0(inX, dataSet, labels, k):
	dataSetSize = dataSet.shape[0]

	#距离计算
	diffMat = tile(inX, (dataSetSize,1)) - dataSet
	sqDiffMat = diffMat**2
	sqDistances = sqDiffMat.sum(axis=1)
	distances = sqDistances**0.5

	sortedDistIndicies = distances.argsort()
	classCount = {}
	for i in range(k):
		voteLable = labels[sortedDistIndicies[i]]
		classCount[voteLable] = classCount.get(voteLable,0) + 1		# 选择距离最小的k个点

	sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse = True)
	return sortedClassCount[0][0]

def file2matrix(filename):
	fr = open(filename)
	arrayOfLines = fr.readlines()
	numberOfLines = len(arrayOfLines)
	returnMat = zeros((numberOfLines, 3))

	classLabelVector = []
	index = 0
	for line in arrayOfLines:
		line = line.strip()
		listFromLine = line.split('\t')
		returnMat[index, :] = listFromLine[0:3]
		classLabelVector.append(int(listFromLine[-1]))
		index += 1

	return returnMat, classLabelVector

# 归一化特征值，将数据特征值转化为0到1的区间
def autoNorm(dataSet):
	minVals = dataSet.min(0)
	maxVals = dataSet.max(0)
	ranges = maxVals - minVals
	normDataSet = zeros(shape(dataSet))
	m = dataSet.shape[0]
	normDataSet = dataSet - tile(minVals, (m,1))
	normDataSet = normDataSet / tile(ranges, (m,1))
	return normDataSet, ranges, minVals


def datingClassTest():
	hoRatio = 0.10
	datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
	normMat, ranges, minVals = autoNorm(datingDataMat)
	m = normMat.shape[0]
	numTestVecs = int(m * hoRatio)
	errorCount = 0.0
	for i in range(numTestVecs):
		classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 4)
		print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
		if (classifierResult != datingLabels[i]): errorCount += 1.0
	print "the total error rate is %f" % (errorCount/float(numTestVecs))


def classifyPerson():
	resultList = ['not at all', 'in small doses', 'in large doses']
	percentTats = float(raw_input("percentage of time spent playing video games?"))
	ffMiles = float(raw_input("frequent flier miles earned per year?"))
	iceCream = float(raw_input("liters of ice cream consumed per year?"))
	datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
	normMat, ranges, minVals = autoNorm(datingDataMat)

	inArr = array([ffMiles, percentTats, iceCream])
	classifierResult = classify0((inArr - minVals)/ranges, normMat, datingLabels, 3)
	print "You will probably like this person: ", resultList[classifierResult - 1]

def img2vector(filename):
    returnVect = zeros((1,1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0, 32*i+j] = int(lineStr[j])
    return returnVect

def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)

    testFileList = listdir('testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        
        if (classifierResult != classNumStr): 
        	errorCount += 1.0
        	print "%s: the classifier came back with: %d, the real answer is: %d" % (fileNameStr, classifierResult, classNumStr)

    print "\nthe total number of errors is: %d" % errorCount
    print "\nthe total error rate is: %f" % (errorCount / float(mTest))

